Deep Learning Approaches for Financial Volatility Forecasting and Market Stress Detection
Keywords:
Financial Volatility, Market Stress Detection, Deep Learning Systems, Algorithmic Governance, Socio-Technical Infrastructure, Systemic Risk.Abstract
Financial volatility forecasting and the detection of market stress represent critical pillars of contemporary global economic stability and institutional risk management. Traditional econometric models, predominantly rooted in the autoregressive conditional heteroskedasticity family, have historically provided a foundational understanding of price fluctuations but often struggle with the non-linearities, high-frequency noise, and structural breaks inherent in modern, interconnected markets. This paper investigates the shift toward deep learning architectures—specifically encompassing recurrent, convolutional, and attention-based systems—as a transformative approach to predicting market turbulence and identifying systemic stressors. We conduct a system-level analysis that transcends mere predictive accuracy, focusing instead on the socio-technical complexities of deploying deep learning in financial infrastructures. The discussion emphasizes the architectural trade-offs between model depth and interpretability, the governance challenges posed by black-box algorithms in regulated environments, and the physical infrastructure required to sustain such high-compute operations. Furthermore, the research addresses the socio-economic implications of algorithmic convergence, where the widespread adoption of similar deep learning models may inadvertently exacerbate market fragility during tail-risk events. By synthesizing perspectives from systems engineering, financial policy, and artificial intelligence, we propose a multi-dimensional framework for robust volatility modeling that accounts for sustainability, fairness, and systemic resilience. This investigation concludes with a series of forward-looking perspectives on the role of autonomous stress detection in maintaining market integrity amidst increasing global volatility.
References
1.Abadie, A. (2021). Using machine learning for volatility estimation and prediction. Journal of Economic Literature, 59(2), 606-640.
2.Qi, R. (2025, June). Enterprise financial distress prediction based on machine learning and SHAP interpretability analysis. In Proceedings of the 2025 International Conference on Artificial Intelligence and Digital Finance (pp. 76-79).
3.Arratia, A. (2014). Computational Finance: An Introductory Course with R. Atlantis Press.
4.Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.
5.Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
6.Borio, C., & Drehmann, M. (2009). Towards an operational framework for financial stability: ‘fuzzy’ measurement and its consequences. BIS Working Papers, No. 284.
7.Brock, W. A., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764.
8.Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
9.Li, H., & Liu, T. (2023). Portfolio optimization based on the LSTM forecasting model. In Proceedings of the 2nd International Conference on Financial Technology and Business Analysis (Vol. 48, No. 1, pp. 97-106).
10.Cont, R. (2001). Empirical properties of asset returns: Stylized facts and statistical issues. Quantitative Finance, 1(2), 223-236.
11.Dauphin, Y. N., et al. (2017). Language modeling with gated convolutional networks. International Conference on Machine Learning.
12.Diebold, F. X., & Yilmaz, K. (2012). Better to give than to receive: Predictive directional measurement of volatility spillovers. International Journal of Forecasting, 28(1), 57-66.
13.Engst, J. (2020). Deep Learning in Finance: A Practical Guide. Springer.
14.Fischer, T., & Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669.
15.Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
16.Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020). Design and Optimization of Shallow-Angle Grating Coupler for Vertical Emission from Indium Phosphide Devices.
17.Zhou, D. (2025, December). M-VP2: Microservice-Oriented Vulnerability Patch Planning-A Cost-Aware Approachusing Multi-Agent Reinforcement Learning. In 2025 5th International Conference on Computer, Internet of Things and Control Engineering (CITCE) (pp. 248-254). IEEE.
18.Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273.
19.Liu, T. (2026). PCA-APT Stress Index for Market Drawdowns.
20.He, K., et al. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
21.Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.
22.Yi, X. (2026). Trusted AI Commercialization Infrastructure for SMBs: A Unified Multi-Tenant Architecture Integrating Incentive Systems, Content Governance, and Standardized Recommendation APIs.
23.Hull, J. C. (2021). Machine Learning in Business: An Introduction to the World of Data Science. Pearson.
24.Kim, K. (2003). Financial time series forecasting using support vector machines. Neurocomputing, 55(1-2), 307-319.
25.Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
26.Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: A survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209.
27.Yi, X. (2025, October). Compliance-by-Design Micro-Licensing for AI-Generated Content in Social Commerce Using C2PA Content Credentials and W3C ODRL Policies. In 2025 7th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) (pp. 204-208). IEEE.
28.Lopez de Prado, M. (2018). Advances in Financial Machine Learning. John Wiley & Sons.
29.Makridakis, S., Spiliotis, E., & Assimakopoulos, V. (2018). Statistical and Machine Learning forecasting methods: Concerns and ways forward. PLOS ONE, 13(3), e0194889.
30.Zhang, T. (2025). A Knowledge Graph-Enhanced Multimodal AI Framework for Intelligent Tax Data Integration and Compliance Enhancement. Frontiers in Business and Finance, 2(02), 247-261.
31.Paszke, A., et al. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems.
32.Rossi, G. (2018). Socio-Technical Systems and the Finance Industry. Routledge.
33.Qi, R. (2025, July). DecisionFlow for SMEs: A lightweight visual framework for multi-task joint prediction and anomaly detection. In Proceedings of the 2025 International Conference on Economic Management and Big Data Application (pp. 899-903).
34.Schwartz, R., et al. (2020). Green AI. Communications of the ACM, 63(12), 54-63.
35.Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
36.Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
37.Zhang, G. P. (2003). Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing, 50, 159-175.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 International Journal of Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
